Github of this project

(https://github.com/RipplB/sflandingandcompliance)

Initializing block - imports, constants, config

First, I import all the necessary packages installed with the pip tool. Than I set the constants that get used throughout the whole workbook. I also call necessary functions of the libraries to work the way I want, and create useful wrapper functions.

import geopandas as gpd
import pandas as pd
import seaborn as sns
import plotly.express as px
import plotly
import matplotlib.pylab as plt

DATA_DIR = "data"
FIG_DIR = "figures"

plotly.offline.init_notebook_mode()

def save_figure(figure, name):
    figure.savefig(f"{FIG_DIR}/{name}.png", bbox_inches="tight")

Read all data files

The first is the gejson used by the map at the end. Than I load both dataframes. I add an extra column to the noise complaint frame for ease of joining with the other frame.

• Landing data
• Noise complaint data
• Geojson

Please keep in mind, that San Francisco Government might change the url of the sources and they do not offer permalinks i could include here.

california = gpd.read_file(f"{DATA_DIR}/ca_geo.json")
raw_noise_df = pd.read_csv(f"{DATA_DIR}/Aircraft_Noise_Complaint_Data.csv")
raw_landing_df = pd.read_csv(f"{DATA_DIR}/Air_Traffic_Landings_Statistics.csv")

raw_noise_df["Period"] = raw_noise_df["Year"] * 100 + raw_noise_df["Month"]

Clear

In this section I rename columns to a shorter version. I also prefilter the landing data based on it's time period to match the other data frame's period. Than I show a simple line plot for the total complaints to see if there is a rather inactive period.

landing_df = raw_landing_df[["Activity Period", "Operating Airline IATA Code", "GEO Region", "Landing Aircraft Type", "Aircraft Body Type", "Aircraft Model", "Landing Count"]]\
    .rename(columns={"Activity Period": "Period", "Operating Airline IATA Code": "Airline", "GEO Region": "From", "Landing Aircraft Type": "Type", "Aircraft Body Type": "Body", "Aircraft Model": "Model"})\
    .query("Period <= 201907")
noise_df = raw_noise_df[["Period", "Community", "Total Complaints"]]
ax = sns.lineplot(noise_df, x="Period", y="Total Complaints")
save_figure(ax.get_figure(), "fig1")

As seen above, complaints are not really frequent before 2015. Let's have a closer look with a barplot, to find if april of 2015 seems like a good starting point.

plt.figure(figsize=(20, 3))
plt.xticks(rotation=90)
ax = sns.barplot(noise_df.groupby("Period", as_index=False).sum("Total Complaints").query("Period > 201504"), x="Period", y="Total Complaints")
save_figure(ax.get_figure(), "fig2")

As that seems like a good timeframe, I query both dataframes for this time period. After that, I create a barplot to see if the landing counts are any interesting on their own.

landing_df = landing_df.query("Period > 201504")
noise_df = noise_df.query("Period > 201504")
complaints_over_periods_df = noise_df.groupby("Period", as_index=False).sum("Total Complaints")
plt.figure(figsize=(20, 3))
plt.xticks(rotation=90)
ax = sns.barplot(landing_df[["Period", "Landing Count"]].groupby("Period", as_index=False).sum(), x="Period", y="Landing Count")
save_figure(ax.get_figure(), "fig3")

Turns out, that there is a slight repeating trend throughout the year, where february has the lowest, and august the highest traffic. The years itself seem pretty identical.

data = complaints_over_periods_df.sort_values(by=["Period"], ascending=False)
def period_totalcomplaints_horizontal_barplot(ax):
    ax.barh([i for i in range(len(data["Period"]))], data["Total Complaints"], align="edge", color="green")

def pivot_with_complaints(pivot):
    fig, (ax1, ax2) = plt.subplots(1, 2, width_ratios=[1.5, 1], sharey=True, gridspec_kw={"wspace":0})
    fig.set_size_inches(20, 9)
    sns.heatmap(pivot, ax=ax1, cbar_kws={"location":"left", "pad":0.18}, robust=True, linewidths=0.02, linecolor="black")
    period_totalcomplaints_horizontal_barplot(ax2)
    return fig

def landing_column_heatmap_with_complaints(col, filter_arr=None, period_start=None):
    data = landing_df[["Period", col, "Landing Count"]]
    if filter_arr is not None:
        data = data.query(f"{col} in {filter_arr}")
    if period_start is not None:
        data = data.query(f"Period >= {period_start}")
    return pivot_with_complaints(data.groupby(["Period", col], as_index=False).sum().pivot(index="Period", columns=col, values="Landing Count"))

airline_pivot = \
    landing_df[["Period", "Airline", "Landing Count"]].groupby(["Period", "Airline"], as_index=False).sum()\
        .pivot(index="Period", columns="Airline", values="Landing Count")

plt.figure(figsize=(20, 8))
plt.xticks(rotation=90)
ax = sns.heatmap(airline_pivot, vmax=700, cmap=sns.blend_palette(["#452103", "#F26419"], as_cmap=True))
save_figure(ax.get_figure(), "fig4")

fig = landing_column_heatmap_with_complaints("Airline", ["AA", "AC", "AS", "B6", "CP", "DL", "F9", "OO", "QK", "QX", "UA", "US", "VX", "WN"])
save_figure(fig, "fig5")

fig = landing_column_heatmap_with_complaints("From")
save_figure(fig, "fig6")

fig = landing_column_heatmap_with_complaints("Type")
save_figure(fig, "fig7")

fig = landing_column_heatmap_with_complaints("Body")
save_figure(fig, "fig8")

fig = landing_column_heatmap_with_complaints("Model", filter_arr=["A319", "A321", "A320", "B738", "B739", "B752", "B753", "B772", "B773", "B789", "CRJ2", "CRJ7", "E170", "E175"], period_start="201603")
save_figure(fig, "fig9")

e175_df = landing_df.query("Model == 'E175' & Period != 201712")[["Period", "Landing Count"]].groupby("Period", as_index=False).sum().merge(noise_df, on="Period")
e175_df["Period"] = e175_df["Period"].apply(lambda x: str(x))
active_communities = e175_df[["Period", "Community", "Total Complaints"]].groupby(by=["Period", "Community"], as_index=False).max().query("`Total Complaints` > 10000")["Community"].drop_duplicates()

fig, ax1 = plt.subplots()
fig.set_size_inches(20, 5)
#plt.figure(figsize=(20, 5))
plt.xticks(rotation=90)
ax1.set_ylabel("Total Complaints")
sns.lineplot(data=e175_df.query("Community in @active_communities").pivot(index="Period", columns="Community", values="Total Complaints"), ax=ax1)
plt.legend(bbox_to_anchor=(1.06, 1), loc='upper left', borderaxespad=0)

ax2 = ax1.twinx()
ax2.set_ylabel("Landing Count of E175")
sns.lineplot(data=e175_df[["Period", "Landing Count"]].drop_duplicates(), x="Period", y="Landing Count", ax=ax2, color="red", markers=True)
save_figure(fig, "fig10")

useful_cities_geof = california.query("name in @active_communities")
data = e175_df.query("Period > '201808'")
fig = px.choropleth_mapbox(data,
                    geojson=useful_cities_geof,
                    color="Total Complaints",
                    range_color=[0, data.get("Total Complaints").max()],
                    opacity=0.5,
                    animation_frame="Period",
                    locations="Community",
                    featureidkey="properties.name",
                    zoom=8.5,
                    center={"lat": 37.766801, "lon": -122.176573},
                  mapbox_style="open-street-map", width=900, height=900)
fig.write_html(f"{FIG_DIR}/map.html")
plotly.offline.iplot(fig)